Linearized radiative transfer models for retrieval of cloud parameters from EPIC/DSCOVR measurements. (July 2018)

• Record Type: Journal Article
• Authors: Molina García, Víctor; Sasi, Sruthy; Efremenko, Dmitry S.; Doicu, Adrian; Loyola, Diego
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 213(2018)
• Page Start: 241
• Page End: 251
• Publication Date: 2018-07
• Subjects: Inversion -- Linearized models -- Adjoint radiative transfer -- EPIC; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2018.03.008
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: EPIC Jacobians are computed by conventional and forward-adjoint differentiation. The k -distribution method is used together with the principal component analysis. The performance enhancement of three orders of magnitude is obtained. Abstract: In this paper, we describe several linearized radiative transfer models which can be used for the retrieval of cloud parameters from EPIC (Earth Polychromatic Imaging Camera) measurements. The approaches under examination are (1) the linearized forward approach, represented in this paper by the linearized discrete ordinate and matrix operator methods with matrix exponential, and (2) the forward-adjoint approach based on the discrete ordinate method with matrix exponential. To enhance the performance of the radiative transfer computations, the correlated k -distribution method and the Principal Component Analysis (PCA) technique are used. We provide a compact description of the proposed methods, as well as a numerical analysis of their accuracy and efficiency when simulating EPIC measurements in the oxygen A-band channel at 764 nm. We found that the computation time of the forward-adjoint approach using the correlated k -distribution method in conjunction with PCA is approximately 13 s for simultaneously computing the derivatives with respect to cloud optical thickness and cloud top height.